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Information Fusion on Human Disease Network in Taiwan

Information Fusion on Human Disease Network in Taiwan. Kuang -Chi Chen, Ph.D. Dept. of Medical Informatics, Tzu Chi University, Taiwan DIMACS WAIF, Nov. 8-9, 2012. Outline. Introduction Material and method - Measure of co-morbidity - Taiwan health insurance dataset

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Information Fusion on Human Disease Network in Taiwan

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  1. Information Fusion on Human Disease Network in Taiwan Kuang-Chi Chen, Ph.D. Dept. of Medical Informatics, Tzu Chi University, Taiwan DIMACS WAIF, Nov. 8-9, 2012

  2. Outline • Introduction • Material and method - Measure of co-morbidity - Taiwan health insurance dataset - Rank-combination method • Result and conclusion

  3. Introduction • In organisms, most cellular components exert their functions through interactions with other cellular components. In human, the totality of these interactions representing the human interactome. • Network-based approach is promising to explore the interaction of cellular components, such as genes, proteins, metabolites, SNPs. [References below] References: Barabási A-L. Network Medicine - From Obesity to the “Diseasome”. NEJM. 2007; 357:404. Duarte NC, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS. 2007; 104:1777. Goh K-I, et al. The human disease network. PNAS. 2007; 104:8685–90. Ideker T, Sharan R. Protein networks in disease. Genome Research. 2008; 18:644–52. Jeong H, et al. The large-scale organization of metabolic networks. Nature. 2000; 407:651–4. Oti M, et al. Predicting disease genes using protein-protein interactions. J Med Genet. 2006; 43:691–698. Rual J-F, et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature. 2005; 437:1173–8. Stelzl U, et al. A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome. Cell. 2005; 122:957–68. Xu J, Li Y. Discovering disease-genes by topological features in human protein–protein interaction network. Bioinformatics. 2006; 22.

  4. Examples of cellular network • For example, - protein interaction networks, whose nodes are proteins linked to each other via physical (binding) interactions; - metabolic networks, whose nodes are metabolites linked if they participate in the same biochemical reactions; - genetic networks, in which two genes are linked if the phenotype of a double mutant differs from the expected phenotype of two single mutants.

  5. Human disease network • Network-based approaches to human disease can have multiple biological and clinical applications, offering a quantitative platform to address the complexity of human disease. In addition, network is also used to explore the relations among diseases by analyzing high-throughput clinical records. • Hidalgo et al. (2009) used 32-million US Medicare records of 65+ elders to build the human disease network (HDN). • In their HDN, nodes are diseases; links are correlations between a pair of diseases. Reference: Hidalgo CA et al. A dynamic network approach for the study of human phenotypes. PLoS Comp Biol. 2009; e1000353

  6. Human disease network Node color identifies the disease category; node size is proportional to disease prevalence. Link color and weight indicate correlation strength. [Hidalgo et al., PLoS Comp Biol, 2009]

  7. Correlation between diseases • A co-morbidity relationship exists between two diseases whenever they affect the same individual substantially more than chance alone. • Co-morbidity is measured by either φ-correlation or relative risk (RR). • Patient clinical histories contain information on disease associations and progression. The HDN is built by summarizing associations obtained from medical records of millions patients.

  8. Co-morbidity: φ-correlation • Two measures: φ-correlation and Relative Risk (RR). For a pair of diseases i and j, where Cij is the number of patients affected by both i and j, N is the total number of patients in the data, Pi is the prevalence of disease i (# of patients affected by i), Pj is the prevalence of disease j.

  9. Co-morbidity: RR • Relative Risk (RR; denoted by Rij) For a pair of diseases i and j, Rij = 1 imply no co-morbidity; Rij > 1 imply positive co-morbidity; 0 < Rij < 1 imply negative co-morbidity. Similarly,φij = 0 imply no co-morbidity; 0 < φij < 1 imply positive co-morbidity; -1 < φij < 0 imply negative co-morbidity.

  10. Taiwan Health Insurance database (I) • Taiwan launched a single-payer National Health Insurance program on March 1, 1995. As of 2007, 22.60 million of Taiwan’s 22.96 million population were enrolled in this program. (coverage = 98.4%) • Large computerized databases derived from this system by the Bureau of National Health Insurance, Taiwan (BNHI). • The database of this program contains registration files and original claim data for reimbursement. http://w3.nhri.org.tw/nhird/en/Data_Files.html

  11. Taiwan Health Insurance database (II) • These data files are de-identified by scrambling the identification codes of both patients and medical facilities. • The data can be applied for research purpose only. They are data subsets extracted by systematic sampling method of 0.2% (outpatient records) or 5% (inpatient records) on a monthly basis. • There was no significant difference in the gender distribution between the data subsets and the original claim data.

  12. Inpatient data subset • We use Taiwan inpatient data subset to measure co-morbidity and build the HDN. • A primary diagnosis and up to 4 secondary diagnoses are specified by ICD-9-CM codes. • ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) is set up by the WHO (World Health Organization). ICD-9-CM is the official system of assigning 5-digit codes to diagnoses and procedures associated with hospital utilization in many countries. http://www.cdc.gov/nchs/icd/icd9cm.htm

  13. ICD-9-CM • The first three digits specify the main disease category while the last two provide additional information about the disease. • In total, ICD-9-CM classification consists of 17 chapters, 657 categories of diseases at the 3 digit level and 16,459 categories at 5 digits. • Disregard of complications of pregnancy, childbirth, and the puerperium (630-679), congenital anomalies (740-759), newborn perinatal guidelines (760-779), and injury and poisoning (800-999), 635 disease categories are considered.

  14. Descriptive statistics of inpatient data • There are 4,287,191 individuals with 8,927,522 hospitalization claims during 3 years are used in our study. • The mean age is 45.10 years old, and the percentage of female is 52.17%. • To measure association resulting in disease co-occurrence, we use φ-correlation and Relative Risk (RR) to quantify the strength of co-morbidity based on Taiwan inpatient data subset.

  15. Taiwan human disease network • There are 635 disease nodes, and C6352 = 201,295 possible links are considered in the HDN. (huge # of edges) • Two nodes are linked if the association measure > threshold (preconceived or decided by statistical testing) For example, • φ –correlation: two diseases are associated if φ > 0.06 • RR: two diseases are associated if R > 20 where 0.06 and 20 are suggested by Hidalgo et al. (2009)

  16. Problem and goal • However, these measures have biases that over- or under-estimate the co-morbidity involving rare or prevalent diseases (i.e., low or high prevalence). - φ-correlation under-estimates the strength of co-morbidity between rare and prevalent diseases, - RR under-estimates the co-morbidity strength between two prevalent diseases, - RR over-estimates the co-morbidity involving rare diseases. • We propose a rank-combination method to reduce the biases regarding prevalence.

  17. Distribution of prevalence of 635 disease categories Low 13.23% (rare disease) Middle 61.57% High 25.20% (prevalent disease) # diseases 0 10 102 103 104 105 Prevalence

  18. Distribution of φ-correlation Cons: under-estimation between rare and prevalent diseases # of pairs high vs. high low vs. low high vs. low φ φ φ φtends to be small for rare disease vs. prevalence disease

  19. Distribution of RR Cons:under-estimation between prevalent diseases over-estimation when involving rare diseases mid. vs. mid. high vs. high high vs. low low vs. low # of pairs log10(R) log10(R) log10(R) log10(R) skewed to the left skewed to the right extremely high

  20. Rank-score functions Normalized log10(R) Normalizedφ 1 .5 0 1 .5 0 Two measures (φ and RR) look quite diverse. Rank Rank The score is normalized into [0, 1] by (score - min)/(max – min)

  21. Rank-combination method • Two diseases are associated if [Rank(φ) + Rank(R)]/2 < threshold-rank • The threshold-rank is decided as follows: There are 1,230 significant associations with Φ> 0.06, and 2,749 significant associations with R > 20. Among 201,295 possible links from 635 disease nodes, the significant levels are 0.61% and 1.37%. • Choose (1230 + 2749)/2 ≒ 1990 as the threshold-rank. (0.99% significant level) • Two diseases are associated if [Rank(φ) + Rank(R)]/2 < 1990

  22. Rank-combination vs. φ-correlation • Since φ-correlation under-estimates the co-morbidity between rare and prevalent diseases, there are 0% significant associations. • Rank-combination reduces the biases by raising the significant rate to 0.1%. • In addition, the variation of significant percentages (0.6%, 0.1%, and 1.7%) is smaller than that of 0.2%, 0%, and 6.2%. • Rank-combination method is robust to different prevalence.

  23. Rank-combination vs. RR • RR under-estimates the co-morbidity between two prevalent diseases, • but over-estimates the co-morbidity involving rare diseases. • Rank-combinationreduces the biases, and the variation of significant percentages becomes smaller. • Rank-combination method is robust to extreme prevalence.

  24. Disease network by rank-combination red link – strong association pink link – middle association grey link – low association Node color identifies the disease category; node size is proportional to disease prevalence. Link color and weight indicate correlation strength.

  25. Conclusion • No best measure for co-morbidity. • No true answer for link or not between diseases. • Both φ-correlation and RR are good at measuring co-morbidity and have limitation in measuring co-morbidity. They are complementary to each other. • Rank-combination method by combining two ranks of measures overcomes the disadvantages of φ-correlation as well as RR. • The links of disease network decided by rank-combination method are more reliable.

  26. Acknowledgements • Thanks to funders: - National Science Council (NSC) in Taiwan - Tzu-Chi University, Hualien, Taiwan • Data Providers: - National Health Research Institute (NHRI), Taiwan - Bureau of National Health Insurance (BNHI),Taiwan • Collaborators: - Dr. Tse-Yi Wang, Ma-Kai Memorial Hospital, Taipei - Dr. Chen-hsuiang Chan, Tzu-Chi University, Hualien - Mr. Yao-Hung Hsaio, Ms. Sin-Yi Wang, Ms. Yi-Ro Lin

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